Cue and Sensor Fusion for Independent Moving Objects Detection and Description in Driving Scenes

In this study we present an approach to detecting, describing and tracking independently moving objects (IMOs) in stereo video sequences acquired by on-board cameras on a moving vehicle. In the proposed model only three sensors are used: stereovision, speedometer and LIDAR (Light Detection and Ranging). The IMOs detected by vision are matched with obstacles provided by LIDAR. In the case of a successful matching, the descriptions of the IMOs (distance, relative speed and acceleration) are provided by ACC (Adaptive Cruise Control) LIDAR sensor, or otherwise these descriptions are estimated based on vision. Absolute speed of the IMO is evaluated using its relative velocity and egospeed provided by the speedometer. Preliminary results indicate the generalization ability of the proposed system.

[1]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[2]  Werner von Seelen,et al.  FUSION OF DIFFERENT SENSORS AND ALGORITHMS FOR SEGMENTATION , 1998 .

[3]  Marc M. Van Hulle,et al.  Segmenting Independently Moving Objects from Egomotion Flow Fields , 2004 .

[4]  Michalis E. Zervakis,et al.  A survey of video processing techniques for traffic applications , 2003, Image Vis. Comput..

[5]  Bernt Schiele,et al.  Scale-Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search , 2004, DAGM-Symposium.

[6]  Marc M. Van Hulle,et al.  Optic Flow from Unstable Sequences containing Unconstrained Scenes through Local Velocity Constancy Maximization , 2006, BMVC.

[7]  Y. Tamatsu,et al.  Solid or not solid: vision for radar target validation , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[8]  J. Laneurit,et al.  Multisensorial data fusion for global vehicle and obstacles absolute positioning , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[9]  C. Blanc,et al.  Track to track fusion method applied to road obstacle detection , 2004 .

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Dominique Gruyer,et al.  Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner , 2005, Auton. Robots.

[12]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[13]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[14]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[15]  Max Donath,et al.  BUS RAPID TRANSIT TECHNOLOGIES: A VIRTUAL MIRROR FOR ELIMINATING VEHICLE BLIND ZONES, VOLUME 2 , 2005 .

[16]  J. Tukey,et al.  The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data , 1974 .

[17]  I. Masaki,et al.  An obstacle detection method by fusion of radar and motion stereo , 2002, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[18]  M.M. Van Hulle,et al.  An Approach to On-Road Vehicle Detection, Description and Tracking , 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

[19]  Yajun Fang,et al.  Depth-based target segmentation for intelligent vehicles: fusion of radar and binocular stereo , 2002, IEEE Trans. Intell. Transp. Syst..

[20]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[21]  Tarak Gandhi,et al.  Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps , 2006, IEEE Transactions on Intelligent Transportation Systems.

[22]  Massimo Bertozzi,et al.  Artificial vision in road vehicles , 2002, Proc. IEEE.

[23]  C. Laurgeau,et al.  Fade: a vehicle detection and tracking system featuring monocular color vision and radar data fusion , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[24]  Fabio Solari,et al.  Compact (and accurate) early vision processing in the harmonic space , 2007, VISAPP.

[25]  Ernst D. Dickmanns,et al.  Radar and Vision Data Fusion for Hybrid Adaptive Cruise Control on Highways , 2001, ICVS.

[26]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[27]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[28]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Belur V. Dasarathy,et al.  Sensor fusion potential exploitation-innovative architectures and illustrative applications , 1997, Proc. IEEE.

[30]  Jan Becker,et al.  Sensor and navigation data fusion for an autonomous vehicle , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[31]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[32]  Christoph Stiller,et al.  Multisensor obstacle detection and tracking , 2000, Image Vis. Comput..

[33]  Massimo Bertozzi,et al.  Vision-based intelligent vehicles: State of the art and perspectives , 2000, Robotics Auton. Syst..